Research Article An Evolutionary Multiagent Framework for Multiobjective Optimization ZihuiZhang, 1,2 QiaomeiHan, 2 YanqiangLi, 1 YongWang, 1 andYanjunShi 2 1 Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Shandong Provincial Key Laboratory of Automotive Electronics Technology, Jinan, CO 250014, China 2 Mechanical Engineering Department, Dalian University of Technology, Dalian, CO 116024, China Correspondence should be addressed to Yanjun Shi; syj@ieee.org Received 17 September 2019; Revised 1 February 2020; Accepted 2 March 2020; Published 21 April 2020 Academic Editor: Emilio Insfran Pelozo Copyright © 2020 Zihui Zhang et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. is article proposes an evolutionary multiagent framework of the co-operative co-evolutionary multiobjective model (CCMO- EMAS), specifically for equipment layout optimization in engineering. In this framework, each agent is set in a multiobjective cooperative co-evolutionary mode along with the algorithms and corresponding settings. In each iteration, agents are executed in turn, and each agent optimizes a subpopulation from system decomposition. Additionally, the collaboration mechanism is addressed to build complete solutions and evaluate individuals in the co-operative co-evolutionary algorithm. Each subpopulation is optimized once, and the corresponding agent is evaluated based on the improvement of the system memory. Moreover, the agent team is also evolved through an elite genetic algorithm. Finally, the proposed CCMO-EMAS framework is verified in a multimodule satellite equipment layout problem. 1.Introduction On the conception of the agent, researchers presented di- verse viewpoints according to their research fields, and there is still no uniform definition. Currently, using an agent- based model to solve optimization problems attracts the attentions of an increasing number of researchers [1]. e multiagent system (MAS) is the set of several agent elements which interact with each other under some defined rules or given conditions. rough combining multiagent with the evolutionary algorithms, several researchers proposed agent-based evolutionary algorithms [2]. ere are mainly three patterns for agent-based evolu- tionary algorithm. (1) e MAS is used as the management layer to control the evolutionary procedure. In such methods, agents have their functions, and the algorithm optimization is achieved through coordination and inter- action among agent systems. erefore, when solving dif- ferent problems, it is necessary to reestablish the multiagent model according to different situations. For instance, Car- don et al. [3] used GA to solve multiobjective job shop scheduling problems with a multiagent system. (2) e MAS represents the population of the algorithm. e typical method in this field is the multiagent genetic algorithm. As an example, Zhong et al. [4] took each solution in the population as one agent, and all the agents are distributed within the grid in a two-dimension space, with each node only linking with four neighbor nodes. It is equivalent to decompose the solution and searching space, which has a good effect on improving the diversity of the algorithm. is approach is more likely to be regarded as an algorithm rather than a framework. (3) Each agent in MAS represents an algorithm model, where the representative work is the re- search of Hanna and Cagan [5]. is evolutionary multi- agent system (EMAS) is a framework that can integrate many kinds of evolutionary algorithms in MAS and changes the algorithm model by the evolution of agents. EMAS framework adjusts the algorithm model and parameter settings through collaboration and interaction between agents, and obtains a better solution. In the past decades, the EMAS has been successful in solving engineering design problems, especially in terms of Hindawi Mathematical Problems in Engineering Volume 2020, Article ID 9147649, 18 pages https://doi.org/10.1155/2020/9147649